DHA-Net: An encoder-decoder network fusing multi-scale features for optic disc segmentation

Automatic and accurate segmentation of the optic disc (OD) region has practical applications in the medical field. In this study, a novel encoder-decoder network is proposed to segment the ODs automatically and accurately. The encoder consists of three parts: (1) A low-level feature extraction module composed of dense connectivity block (Dense Block) which can output rich low-level features; (2) A High-resolution Block (HR Block) which can extract sufficient semantic information while reducing parameters; (3) An Atrous Spatial Pyramid Pooling (ASPP) module is used to obtain high-level features. Therefore, the network is named DHA-Net. The proposed decoder takes advantage of the multi-scale features from the encoder to predict OD regions. Compared with the existing methods on three datasets, it is proved that the proposed method is better than the current excellent methods in the segmentation results of normal and abnormal fundus. The ablation studies proved the influence of each module on the segmentation performance, and explained the network structure reasonably. In the case of fewer network parameters, DHA-Net achieves better prediction performance on intersection over union (IoU), dice similarity coefficient (DSC) and other evaluation metrics. DHA-Net is lightweight and can use multi-scale features to predict OD regions.